#loading packages
library(tidyverse)
library(lubridate)
library(dplyr)
library(ggplot2)Lab 7
Demo
#import data
download.file(url="https://raw.githubusercontent.com/CSSEGISandData/COVID-19/master/csse_covid_19_data/csse_covid_19_time_series/time_series_covid19_confirmed_global.csv",
destfile = "data/time_series_covid19_confirmed_global.csv")#load data
time_series_confirmed <- read_csv("data/time_series_covid19_confirmed_global.csv")|>
rename(Province_State = "Province/State", Country_Region = "Country/Region")Data Tidying - Pivoting
#convert to long format
time_series_confirmed_long <- time_series_confirmed |>
pivot_longer(-c(Province_State, Country_Region, Lat, Long),
names_to = "Date", values_to = "Confirmed") Dates and Time
#Change the format of Date
time_series_confirmed_long$Date <- mdy(time_series_confirmed_long$Date)Making Graphs From the Time Series Data
#Summarize the country date to count up thee individual state data for the US
time_series_confirmed_long|>
group_by(Country_Region, Date) |>
summarise(Confirmed = sum(Confirmed)) |>
filter (Country_Region == "US") |>
ggplot(aes(x = Date, y = Confirmed)) +
geom_point() +
geom_line() +
ggtitle("US COVID-19 Confirmed Cases")
#Many countries on the same graph
time_series_confirmed_long |>
group_by(Country_Region, Date) |>
summarise(Confirmed = sum(Confirmed)) |>
filter (Country_Region %in% c("China","France","Italy",
"Korea, South", "US")) |>
ggplot(aes(x = Date, y = Confirmed, color = Country_Region)) +
geom_point() +
geom_line() +
ggtitle("COVID-19 Confirmed Cases")
#make new table with the daily counts
time_series_confirmed_long_daily <-time_series_confirmed_long |>
group_by(Country_Region, Date) |>
summarise(Confirmed = sum(Confirmed)) |>
mutate(Daily = Confirmed - lag(Confirmed, default = first(Confirmed )))#Graph with US data
time_series_confirmed_long_daily |>
filter (Country_Region == "US") |>
ggplot(aes(x = Date, y = Daily, color = Country_Region)) +
geom_point() +
ggtitle("COVID-19 Confirmed Cases")
#Line graph version
time_series_confirmed_long_daily |>
filter (Country_Region == "US") |>
ggplot(aes(x = Date, y = Daily, color = Country_Region)) +
geom_line() +
ggtitle("COVID-19 Confirmed Cases")
#with a curve fit
time_series_confirmed_long_daily |>
filter (Country_Region == "US") |>
ggplot(aes(x = Date, y = Daily, color = Country_Region)) +
geom_smooth() +
ggtitle("COVID-19 Confirmed Cases")
#Fit using the Generalized Additive Model (GAM)
time_series_confirmed_long_daily |>
filter (Country_Region == "US") |>
ggplot(aes(x = Date, y = Daily, color = Country_Region)) +
geom_smooth(method = "gam", se = FALSE) +
ggtitle("COVID-19 Confirmed Cases")
Animated Graphs with gganimate
#load the packages
library(gganimate)
library(gifski)
theme_set(theme_bw())An Animation of the Confirmed Cases in Select Countries
daily_counts <- time_series_confirmed_long_daily |>
filter (Country_Region == "US")
p <- ggplot(daily_counts, aes(x = Date, y = Daily, color = Country_Region)) +
geom_point() +
ggtitle("Confirmed COVID-19 Cases") +
# gganimate lines
geom_point(aes(group = seq_along(Date))) +
transition_reveal(Date)
# make the animation
animate(p, renderer = gifski_renderer(), end_pause = 15)
#Save the gif
anim_save("daily_counts_US.gif", p)Animation of Confirmed Deaths
# This download may take about 5 minutes. You only need to do this once so set `#| eval: false` in your qmd file
download.file(url="https://raw.githubusercontent.com/CSSEGISandData/COVID-19/master/csse_covid_19_data/csse_covid_19_time_series/time_series_covid19_deaths_global.csv",
destfile = "data/time_series_covid19_deaths_global.csv")#data tidying, pivot and time
time_series_deaths_confirmed <- read_csv("data/time_series_covid19_deaths_global.csv")|>
rename(Province_State = "Province/State", Country_Region = "Country/Region")
time_series_deaths_long <- time_series_deaths_confirmed |>
pivot_longer(-c(Province_State, Country_Region, Lat, Long),
names_to = "Date", values_to = "Confirmed")
time_series_deaths_long$Date <- mdy(time_series_deaths_long$Date)#making the animated graph
p <- time_series_deaths_long |>
filter (Country_Region %in% c("US","Canada", "Mexico","Brazil","Egypt","Ecuador","India", "Netherlands", "Germany", "China" )) |>
ggplot(aes(x=Country_Region, y=Confirmed, color= Country_Region)) +
geom_point(aes(size=Confirmed)) +
transition_time(Date) +
labs(title = "Cumulative Deaths: {frame_time}") +
ylab("Deaths") +
theme(axis.text.x = element_text(angle = 45, vjust = 1, hjust=1))
# make the animation
animate(p, renderer = gifski_renderer(), end_pause = 15)
Exercises
Exercise 1:
Go through Chapter 5 in R for Data Sciences - Data Tiyding and Pivot](https://r4ds.hadley.nz/data-tidy.html) putting the examples and exerices into your report as in Lab 2 and 3
Chapter 5
5.2
#load packages
library(tidyverse)table1# A tibble: 6 × 4
country year cases population
<chr> <dbl> <dbl> <dbl>
1 Afghanistan 1999 745 19987071
2 Afghanistan 2000 2666 20595360
3 Brazil 1999 37737 172006362
4 Brazil 2000 80488 174504898
5 China 1999 212258 1272915272
6 China 2000 213766 1280428583#> # A tibble: 6 × 4
#> country year cases population
#> <chr> <dbl> <dbl> <dbl>
#> 1 Afghanistan 1999 745 19987071
#> 2 Afghanistan 2000 2666 20595360
#> 3 Brazil 1999 37737 172006362
#> 4 Brazil 2000 80488 174504898
#> 5 China 1999 212258 1272915272
#> 6 China 2000 213766 1280428583
table2# A tibble: 12 × 4
country year type count
<chr> <dbl> <chr> <dbl>
1 Afghanistan 1999 cases 745
2 Afghanistan 1999 population 19987071
3 Afghanistan 2000 cases 2666
4 Afghanistan 2000 population 20595360
5 Brazil 1999 cases 37737
6 Brazil 1999 population 172006362
7 Brazil 2000 cases 80488
8 Brazil 2000 population 174504898
9 China 1999 cases 212258
10 China 1999 population 1272915272
11 China 2000 cases 213766
12 China 2000 population 1280428583#> # A tibble: 12 × 4
#> country year type count
#> <chr> <dbl> <chr> <dbl>
#> 1 Afghanistan 1999 cases 745
#> 2 Afghanistan 1999 population 19987071
#> 3 Afghanistan 2000 cases 2666
#> 4 Afghanistan 2000 population 20595360
#> 5 Brazil 1999 cases 37737
#> 6 Brazil 1999 population 172006362
#> # ℹ 6 more rows
table3# A tibble: 6 × 3
country year rate
<chr> <dbl> <chr>
1 Afghanistan 1999 745/19987071
2 Afghanistan 2000 2666/20595360
3 Brazil 1999 37737/172006362
4 Brazil 2000 80488/174504898
5 China 1999 212258/1272915272
6 China 2000 213766/1280428583#> # A tibble: 6 × 3
#> country year rate
#> <chr> <dbl> <chr>
#> 1 Afghanistan 1999 745/19987071
#> 2 Afghanistan 2000 2666/20595360
#> 3 Brazil 1999 37737/172006362
#> 4 Brazil 2000 80488/174504898
#> 5 China 1999 212258/1272915272
#> 6 China 2000 213766/1280428583# Compute rate per 10,000
table1 |>
mutate(rate = cases / population * 10000)# A tibble: 6 × 5
country year cases population rate
<chr> <dbl> <dbl> <dbl> <dbl>
1 Afghanistan 1999 745 19987071 0.373
2 Afghanistan 2000 2666 20595360 1.29
3 Brazil 1999 37737 172006362 2.19
4 Brazil 2000 80488 174504898 4.61
5 China 1999 212258 1272915272 1.67
6 China 2000 213766 1280428583 1.67 # Visualize changes over time
ggplot(table1, aes(x = year, y = cases)) +
geom_line(aes(group = country), color = "grey50") +
geom_point(aes(color = country, shape = country)) +
scale_x_continuous(breaks = c(1999, 2000)) # x-axis breaks at 1999 and 2000
5.2.1 Exercises
- For each of the sample tables, describe what each observation and each column represents.
For Table 1 the columns represent country, the year of the of cases and population, the number of cases, and the total population for that year.
For table 2 the columns represent country, year of the observation, which type of variable is represented in column 4, the value of the variable mentioned in column 3
For table 3 the columns represent the country which the observations were taken from, the year of the observation, and the ratio of two observation.
Sketch out the process you’d use to calculate the
ratefortable2andtable3. You will need to perform four operations:Extract the number of TB cases per country per year.
Extract the matching population per country per year.
Divide cases by population, and multiply by 10000.
Store back in the appropriate place.
You haven’t yet learned all the functions you’d need to actually perform these operations, but you should still be able to think through the transformations you’d need.
a. Extract the number of TB cases per country per year.
For table2 , I will have to filter out rows where the type = “cases”.
For table3, I have to extract the numerator from the “rate” variable for each row.
b. Extract the matching population per country per year.
For table2, I would filter out the rows where the type = “population”
For table3, I will extract the denominator from the rate variable of each row.
c. Divide cases by population, and multiply by 10000.
For table2 I will divide the value from the part a by the answer from part b.
For table 3, I would multiply the rate variable by 10,000.
d. Store back in the appropriate place.
For table2 I would save the rates in a new rows.
For table 3 I would add another column taking the existing rate column to be per 10,000.
5.3
billboard# A tibble: 317 × 79
artist track date.entered wk1 wk2 wk3 wk4 wk5 wk6 wk7 wk8
<chr> <chr> <date> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
1 2 Pac Baby… 2000-02-26 87 82 72 77 87 94 99 NA
2 2Ge+her The … 2000-09-02 91 87 92 NA NA NA NA NA
3 3 Doors D… Kryp… 2000-04-08 81 70 68 67 66 57 54 53
4 3 Doors D… Loser 2000-10-21 76 76 72 69 67 65 55 59
5 504 Boyz Wobb… 2000-04-15 57 34 25 17 17 31 36 49
6 98^0 Give… 2000-08-19 51 39 34 26 26 19 2 2
7 A*Teens Danc… 2000-07-08 97 97 96 95 100 NA NA NA
8 Aaliyah I Do… 2000-01-29 84 62 51 41 38 35 35 38
9 Aaliyah Try … 2000-03-18 59 53 38 28 21 18 16 14
10 Adams, Yo… Open… 2000-08-26 76 76 74 69 68 67 61 58
# ℹ 307 more rows
# ℹ 68 more variables: wk9 <dbl>, wk10 <dbl>, wk11 <dbl>, wk12 <dbl>,
# wk13 <dbl>, wk14 <dbl>, wk15 <dbl>, wk16 <dbl>, wk17 <dbl>, wk18 <dbl>,
# wk19 <dbl>, wk20 <dbl>, wk21 <dbl>, wk22 <dbl>, wk23 <dbl>, wk24 <dbl>,
# wk25 <dbl>, wk26 <dbl>, wk27 <dbl>, wk28 <dbl>, wk29 <dbl>, wk30 <dbl>,
# wk31 <dbl>, wk32 <dbl>, wk33 <dbl>, wk34 <dbl>, wk35 <dbl>, wk36 <dbl>,
# wk37 <dbl>, wk38 <dbl>, wk39 <dbl>, wk40 <dbl>, wk41 <dbl>, wk42 <dbl>, …billboard |>
pivot_longer(
cols = starts_with("wk"),
names_to = "week",
values_to = "rank"
)# A tibble: 24,092 × 5
artist track date.entered week rank
<chr> <chr> <date> <chr> <dbl>
1 2 Pac Baby Don't Cry (Keep... 2000-02-26 wk1 87
2 2 Pac Baby Don't Cry (Keep... 2000-02-26 wk2 82
3 2 Pac Baby Don't Cry (Keep... 2000-02-26 wk3 72
4 2 Pac Baby Don't Cry (Keep... 2000-02-26 wk4 77
5 2 Pac Baby Don't Cry (Keep... 2000-02-26 wk5 87
6 2 Pac Baby Don't Cry (Keep... 2000-02-26 wk6 94
7 2 Pac Baby Don't Cry (Keep... 2000-02-26 wk7 99
8 2 Pac Baby Don't Cry (Keep... 2000-02-26 wk8 NA
9 2 Pac Baby Don't Cry (Keep... 2000-02-26 wk9 NA
10 2 Pac Baby Don't Cry (Keep... 2000-02-26 wk10 NA
# ℹ 24,082 more rowsbillboard |>
pivot_longer(
cols = starts_with("wk"),
names_to = "week",
values_to = "rank",
values_drop_na = TRUE
)# A tibble: 5,307 × 5
artist track date.entered week rank
<chr> <chr> <date> <chr> <dbl>
1 2 Pac Baby Don't Cry (Keep... 2000-02-26 wk1 87
2 2 Pac Baby Don't Cry (Keep... 2000-02-26 wk2 82
3 2 Pac Baby Don't Cry (Keep... 2000-02-26 wk3 72
4 2 Pac Baby Don't Cry (Keep... 2000-02-26 wk4 77
5 2 Pac Baby Don't Cry (Keep... 2000-02-26 wk5 87
6 2 Pac Baby Don't Cry (Keep... 2000-02-26 wk6 94
7 2 Pac Baby Don't Cry (Keep... 2000-02-26 wk7 99
8 2Ge+her The Hardest Part Of ... 2000-09-02 wk1 91
9 2Ge+her The Hardest Part Of ... 2000-09-02 wk2 87
10 2Ge+her The Hardest Part Of ... 2000-09-02 wk3 92
# ℹ 5,297 more rowsbillboard_longer <- billboard |>
pivot_longer(
cols = starts_with("wk"),
names_to = "week",
values_to = "rank",
values_drop_na = TRUE
) |>
mutate(
week = parse_number(week)
)
billboard_longer# A tibble: 5,307 × 5
artist track date.entered week rank
<chr> <chr> <date> <dbl> <dbl>
1 2 Pac Baby Don't Cry (Keep... 2000-02-26 1 87
2 2 Pac Baby Don't Cry (Keep... 2000-02-26 2 82
3 2 Pac Baby Don't Cry (Keep... 2000-02-26 3 72
4 2 Pac Baby Don't Cry (Keep... 2000-02-26 4 77
5 2 Pac Baby Don't Cry (Keep... 2000-02-26 5 87
6 2 Pac Baby Don't Cry (Keep... 2000-02-26 6 94
7 2 Pac Baby Don't Cry (Keep... 2000-02-26 7 99
8 2Ge+her The Hardest Part Of ... 2000-09-02 1 91
9 2Ge+her The Hardest Part Of ... 2000-09-02 2 87
10 2Ge+her The Hardest Part Of ... 2000-09-02 3 92
# ℹ 5,297 more rowsbillboard_longer |>
ggplot(aes(x = week, y = rank, group = track)) +
geom_line(alpha = 0.25) +
scale_y_reverse()
df <- tribble(
~id, ~bp1, ~bp2,
"A", 100, 120,
"B", 140, 115,
"C", 120, 125
)df |>
pivot_longer(
cols = bp1:bp2,
names_to = "measurement",
values_to = "value"
)# A tibble: 6 × 3
id measurement value
<chr> <chr> <dbl>
1 A bp1 100
2 A bp2 120
3 B bp1 140
4 B bp2 115
5 C bp1 120
6 C bp2 125who2# A tibble: 7,240 × 58
country year sp_m_014 sp_m_1524 sp_m_2534 sp_m_3544 sp_m_4554 sp_m_5564
<chr> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
1 Afghanistan 1980 NA NA NA NA NA NA
2 Afghanistan 1981 NA NA NA NA NA NA
3 Afghanistan 1982 NA NA NA NA NA NA
4 Afghanistan 1983 NA NA NA NA NA NA
5 Afghanistan 1984 NA NA NA NA NA NA
6 Afghanistan 1985 NA NA NA NA NA NA
7 Afghanistan 1986 NA NA NA NA NA NA
8 Afghanistan 1987 NA NA NA NA NA NA
9 Afghanistan 1988 NA NA NA NA NA NA
10 Afghanistan 1989 NA NA NA NA NA NA
# ℹ 7,230 more rows
# ℹ 50 more variables: sp_m_65 <dbl>, sp_f_014 <dbl>, sp_f_1524 <dbl>,
# sp_f_2534 <dbl>, sp_f_3544 <dbl>, sp_f_4554 <dbl>, sp_f_5564 <dbl>,
# sp_f_65 <dbl>, sn_m_014 <dbl>, sn_m_1524 <dbl>, sn_m_2534 <dbl>,
# sn_m_3544 <dbl>, sn_m_4554 <dbl>, sn_m_5564 <dbl>, sn_m_65 <dbl>,
# sn_f_014 <dbl>, sn_f_1524 <dbl>, sn_f_2534 <dbl>, sn_f_3544 <dbl>,
# sn_f_4554 <dbl>, sn_f_5564 <dbl>, sn_f_65 <dbl>, ep_m_014 <dbl>, …who2 |>
pivot_longer(
cols = !(country:year),
names_to = c("diagnosis", "gender", "age"),
names_sep = "_",
values_to = "count"
)# A tibble: 405,440 × 6
country year diagnosis gender age count
<chr> <dbl> <chr> <chr> <chr> <dbl>
1 Afghanistan 1980 sp m 014 NA
2 Afghanistan 1980 sp m 1524 NA
3 Afghanistan 1980 sp m 2534 NA
4 Afghanistan 1980 sp m 3544 NA
5 Afghanistan 1980 sp m 4554 NA
6 Afghanistan 1980 sp m 5564 NA
7 Afghanistan 1980 sp m 65 NA
8 Afghanistan 1980 sp f 014 NA
9 Afghanistan 1980 sp f 1524 NA
10 Afghanistan 1980 sp f 2534 NA
# ℹ 405,430 more rowshousehold# A tibble: 5 × 5
family dob_child1 dob_child2 name_child1 name_child2
<int> <date> <date> <chr> <chr>
1 1 1998-11-26 2000-01-29 Susan Jose
2 2 1996-06-22 NA Mark <NA>
3 3 2002-07-11 2004-04-05 Sam Seth
4 4 2004-10-10 2009-08-27 Craig Khai
5 5 2000-12-05 2005-02-28 Parker Gracie household |>
pivot_longer(
cols = !family,
names_to = c(".value", "child"),
names_sep = "_",
values_drop_na = TRUE
)# A tibble: 9 × 4
family child dob name
<int> <chr> <date> <chr>
1 1 child1 1998-11-26 Susan
2 1 child2 2000-01-29 Jose
3 2 child1 1996-06-22 Mark
4 3 child1 2002-07-11 Sam
5 3 child2 2004-04-05 Seth
6 4 child1 2004-10-10 Craig
7 4 child2 2009-08-27 Khai
8 5 child1 2000-12-05 Parker
9 5 child2 2005-02-28 Gracie5.4
cms_patient_experience# A tibble: 500 × 5
org_pac_id org_nm measure_cd measure_title prf_rate
<chr> <chr> <chr> <chr> <dbl>
1 0446157747 USC CARE MEDICAL GROUP INC CAHPS_GRP… CAHPS for MI… 63
2 0446157747 USC CARE MEDICAL GROUP INC CAHPS_GRP… CAHPS for MI… 87
3 0446157747 USC CARE MEDICAL GROUP INC CAHPS_GRP… CAHPS for MI… 86
4 0446157747 USC CARE MEDICAL GROUP INC CAHPS_GRP… CAHPS for MI… 57
5 0446157747 USC CARE MEDICAL GROUP INC CAHPS_GRP… CAHPS for MI… 85
6 0446157747 USC CARE MEDICAL GROUP INC CAHPS_GRP… CAHPS for MI… 24
7 0446162697 ASSOCIATION OF UNIVERSITY PHYSI… CAHPS_GRP… CAHPS for MI… 59
8 0446162697 ASSOCIATION OF UNIVERSITY PHYSI… CAHPS_GRP… CAHPS for MI… 85
9 0446162697 ASSOCIATION OF UNIVERSITY PHYSI… CAHPS_GRP… CAHPS for MI… 83
10 0446162697 ASSOCIATION OF UNIVERSITY PHYSI… CAHPS_GRP… CAHPS for MI… 63
# ℹ 490 more rowscms_patient_experience |>
distinct(measure_cd, measure_title)# A tibble: 6 × 2
measure_cd measure_title
<chr> <chr>
1 CAHPS_GRP_1 CAHPS for MIPS SSM: Getting Timely Care, Appointments, and Infor…
2 CAHPS_GRP_2 CAHPS for MIPS SSM: How Well Providers Communicate
3 CAHPS_GRP_3 CAHPS for MIPS SSM: Patient's Rating of Provider
4 CAHPS_GRP_5 CAHPS for MIPS SSM: Health Promotion and Education
5 CAHPS_GRP_8 CAHPS for MIPS SSM: Courteous and Helpful Office Staff
6 CAHPS_GRP_12 CAHPS for MIPS SSM: Stewardship of Patient Resources cms_patient_experience |>
pivot_wider(
names_from = measure_cd,
values_from = prf_rate
)# A tibble: 500 × 9
org_pac_id org_nm measure_title CAHPS_GRP_1 CAHPS_GRP_2 CAHPS_GRP_3
<chr> <chr> <chr> <dbl> <dbl> <dbl>
1 0446157747 USC CARE MEDICA… CAHPS for MI… 63 NA NA
2 0446157747 USC CARE MEDICA… CAHPS for MI… NA 87 NA
3 0446157747 USC CARE MEDICA… CAHPS for MI… NA NA 86
4 0446157747 USC CARE MEDICA… CAHPS for MI… NA NA NA
5 0446157747 USC CARE MEDICA… CAHPS for MI… NA NA NA
6 0446157747 USC CARE MEDICA… CAHPS for MI… NA NA NA
7 0446162697 ASSOCIATION OF … CAHPS for MI… 59 NA NA
8 0446162697 ASSOCIATION OF … CAHPS for MI… NA 85 NA
9 0446162697 ASSOCIATION OF … CAHPS for MI… NA NA 83
10 0446162697 ASSOCIATION OF … CAHPS for MI… NA NA NA
# ℹ 490 more rows
# ℹ 3 more variables: CAHPS_GRP_5 <dbl>, CAHPS_GRP_8 <dbl>, CAHPS_GRP_12 <dbl>cms_patient_experience |>
pivot_wider(
id_cols = starts_with("org"),
names_from = measure_cd,
values_from = prf_rate
)# A tibble: 95 × 8
org_pac_id org_nm CAHPS_GRP_1 CAHPS_GRP_2 CAHPS_GRP_3 CAHPS_GRP_5 CAHPS_GRP_8
<chr> <chr> <dbl> <dbl> <dbl> <dbl> <dbl>
1 0446157747 USC C… 63 87 86 57 85
2 0446162697 ASSOC… 59 85 83 63 88
3 0547164295 BEAVE… 49 NA 75 44 73
4 0749333730 CAPE … 67 84 85 65 82
5 0840104360 ALLIA… 66 87 87 64 87
6 0840109864 REX H… 73 87 84 67 91
7 0840513552 SCL H… 58 83 76 58 78
8 0941545784 GRITM… 46 86 81 54 NA
9 1052612785 COMMU… 65 84 80 58 87
10 1254237779 OUR L… 61 NA NA 65 NA
# ℹ 85 more rows
# ℹ 1 more variable: CAHPS_GRP_12 <dbl>df <- tribble(
~id, ~measurement, ~value,
"A", "bp1", 100,
"B", "bp1", 140,
"B", "bp2", 115,
"A", "bp2", 120,
"A", "bp3", 105
)df |>
pivot_wider(
names_from = measurement,
values_from = value
)# A tibble: 2 × 4
id bp1 bp2 bp3
<chr> <dbl> <dbl> <dbl>
1 A 100 120 105
2 B 140 115 NAdf |>
distinct(measurement) |>
pull()[1] "bp1" "bp2" "bp3"df |>
select(-measurement, -value) |>
distinct()# A tibble: 2 × 1
id
<chr>
1 A
2 B df |>
select(-measurement, -value) |>
distinct() |>
mutate(x = NA, y = NA, z = NA)# A tibble: 2 × 4
id x y z
<chr> <lgl> <lgl> <lgl>
1 A NA NA NA
2 B NA NA NA df <- tribble(
~id, ~measurement, ~value,
"A", "bp1", 100,
"A", "bp1", 102,
"A", "bp2", 120,
"B", "bp1", 140,
"B", "bp2", 115
)df |>
pivot_wider(
names_from = measurement,
values_from = value
)# A tibble: 2 × 3
id bp1 bp2
<chr> <list> <list>
1 A <dbl [2]> <dbl [1]>
2 B <dbl [1]> <dbl [1]>df |>
group_by(id, measurement) |>
summarize(n = n(), .groups = "drop") |>
filter(n > 1)# A tibble: 1 × 3
id measurement n
<chr> <chr> <int>
1 A bp1 2Exercise 2
Instead of making a graph of 5 countries on the same graph as in the above example, use facet_wrap with scales="free_y".
time_series_confirmed_long |>
group_by(Country_Region, Date) |>
summarise(Confirmed = sum(Confirmed)) |>
filter (Country_Region %in% c("China","France","Italy",
"Korea, South", "US")) |>
ggplot(aes(x = Date, y = Confirmed, color = Country_Region)) +
geom_point() +
geom_line() +
facet_wrap(~ Country_Region, scales = "free_y") +
ggtitle("COVID-19 Confirmed Cases")
Exercise 3
Using the daily count of confirmed cases, make a single graph with 5 countries of your choosing.
time_series_confirmed_long |>
group_by(Country_Region, Date) |>
summarise(Confirmed = sum(Confirmed)) |>
filter (Country_Region %in% c("Albania","Eritrea","Burma",
"Afghanistan", "Japan")) |>
ggplot(aes(x = Date, y = Confirmed, color = Country_Region)) +
geom_point() +
geom_line() +
ggtitle("COVID-19 Confirmed Cases")
Exercise 4
Plot the cumulative deaths in the US, Canada and Mexico (you will need to download time_series_covid19_deaths_global.csv)
time_series_deaths_long |>
group_by(Country_Region, Date) |>
summarise(Confirmed = sum(Confirmed)) |>
filter (Country_Region %in% c("Canada","Mexico", "US")) |>
ggplot(aes(x = Date, y = Confirmed, color = Country_Region)) +
geom_point() +
geom_line() +
ggtitle("COVID-19 Confirmed Deaths")
Exercise 5
Make a graph with the countries of your choice using the daily deaths data
time_series_deaths_long |>
group_by(Country_Region, Date) |>
summarise(Confirmed = sum(Confirmed)) |>
filter (Country_Region %in% c("Albania","Eritrea","Burma",
"Afghanistan", "Japan")) |>
ggplot(aes(x = Date, y = Confirmed, color = Country_Region)) +
geom_point() +
geom_line() +
ggtitle("COVID-19 Confirmed Cases")
Exercise 6
Make an animation of your choosing (do not use a graph with geom_smooth)
p <- time_series_deaths_long |>
filter (Country_Region %in% c("US","Canada", "Mexico","Burma","Eritrea","Albania","Japan", "Afghanistan", "France", "Ghana" )) |>
ggplot(aes(x=Country_Region, y=Confirmed, color= Country_Region)) +
geom_point(aes(size=Confirmed)) +
transition_time(Date) +
labs(title = "Cumulative Deaths: {frame_time}") +
ylab("Deaths") +
theme(axis.text.x = element_text(angle = 45, vjust = 1, hjust=1))
# make the animation
animate(p, renderer = gifski_renderer(), end_pause = 15)